code: def get_interface(*values):
      """Determines the correct framework to dispatch to given a tensor-like object or a
      sequence of tensor-like objects.
  
      Args:
          *values (tensor_like): variable length argument list with single tensor-like objects
  
      Returns:
          str: the name of the interface
  
      To determine the framework to dispatch to, the following rules
      are applied:
  
      * Tensors that are incompatible (such as Torch, TensorFlow and Jax tensors)
        cannot both be present.
  
      * Autograd tensors *may* be present alongside Torch, TensorFlow and Jax tensors,
        but Torch, TensorFlow and Jax take precendence; the autograd arrays will
        be treated as non-differentiable NumPy arrays. A warning will be raised
        suggesting that vanilla NumPy be used instead.
  
      * Vanilla NumPy arrays and SciPy sparse matrices can be used alongside other tensor objects;
        they will always be treated as non-differentiable constants.
  
      .. warning::
          ``get_interface`` defaults to ``"numpy"`` whenever Python built-in objects are passed.
          I.e. a list or tuple of ``torch`` tensors will be identified as ``"numpy"``:
  
          >>> get_interface([torch.tensor([1]), torch.tensor([1])])
          "numpy"
  
          The correct usage in that case is to unpack the arguments ``get_interface(*[torch.tensor([1]), torch.tensor([1])])``.
  
      """
  
      if len(values) == 1:
          return _get_interface_of_single_tensor(values[0])
  
      interfaces = {_get_interface_of_single_tensor(v) for v in values}
  
      if len(interfaces - {"numpy", "scipy", "autograd"}) > 1:
          # contains multiple non-autograd interfaces
          raise ValueError(
              "Tensors contain mixed types; cannot determine dispatch library"
          )
  
      non_numpy_scipy_interfaces = set(interfaces) - {"numpy", "scipy"}
  
      if len(non_numpy_scipy_interfaces) > 1:
          # contains autograd and another interface
          warnings.warn(
              f"Contains tensors of types {non_numpy_scipy_interfaces}; dispatch will prioritize "
              "TensorFlow, PyTorch, and  Jax over Autograd. Consider replacing Autograd with vanilla NumPy.",
              UserWarning,
          )
  
      if "tensorflow" in interfaces:
          return "tensorflow"
  
      if "torch" in interfaces:
          return "torch"
  
      if "jax" in interfaces:
          return "jax"
  
      if "autograd" in interfaces:
          return "autograd"
  
      return "numpy"
  
file_path: pennylane/pennylane/math/utils.py
imports: import warnings
  import autoray
  import numpy
  from autograd.numpy.numpy_boxes import ArrayBox
  from autoray import numpy
  from .None import single_dispatch
label_desc: Use image node_id 6 for calling a global function with example usage: get_interface() and returns: str, _get_interface_of_single_tensor, str, str, str, str